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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 981990 of 9051 papers

TitleStatusHype
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
Domain-Unified Prompt Representations for Source-Free Domain GeneralizationCode1
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement LearningCode1
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided DiffusionCode1
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Multi-View Collaborative Network EmbeddingCode1
DRA-GRPO: Exploring Diversity-Aware Reward Adjustment for R1-Zero-Like Training of Large Language ModelsCode1
Bilingual Mutual Information Based Adaptive Training for Neural Machine TranslationCode1
FairDiverse: A Comprehensive Toolkit for Fair and Diverse Information Retrieval AlgorithmsCode1
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain AdaptationCode1
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